具有多种信息形式的随机多属性决策方法研究

针对属性值为随机变量、清晰数和模糊数三种信息形式的多属性决策问题,提出了一种决策分析方法.在本文中,首先给出了混合占优准则的描述及相关分析,并提出了混合占优度的定义及其计算公式;然后,依据混合占优准则判断并确定两两方案之间比较的占优关系,进而计算得到两两方案之间比较的混合占优度,并构建相应的混合占优度矩阵,在此基础上,运用PROMETHEE Ⅱ方法得到方案的排序结果.最后,通过一个算例说明了本文给出方法的可行性和有效性.     

Decision making with imprecise probabilities and utilities by means of statistical preference and stochastic dominance

A problem of decision making under uncertainty in which the choice must be made between two sets of alternatives instead of two single ones is considered. A number of choice rules are proposed and their main properties are investigated, focusing particularly on the generalizations of stochastic dominance and statistical preference. The particular cases where imprecision is present in the utilities or in the beliefs associated to two alternatives are considered.     

Multi-criteria semantic dominance: A linguistic decision aiding technique based on incomplete preference information

A linguistic decision aiding technique for multi-criteria decision is presented. We define a relation between alternatives as multi-criteria semantic dominance (MCSD). It adopts the similar ideal of the stochastic dominance by utilizing the partial information of the decision maker’s preference, which is only ordinal or partially cardinal. The MCSD rules based on three typical types of semanteme functions are introduced and proven. By using these rules, all the alternatives under consideration are divided into two mutually exclusive sets called efficient set and inefficient set. The decision maker who has such a semanteme function will never choose the alternative from the corresponding inefficient set as the optimal one. In such a way, when we analyze the linguistic decision information, the inherent fuzziness of preference can be handled and several controversial operations of the linguistic terms can be avoided. An example is also provided to illustrate the procedure of the proposed method.     

INSDECM—an interactive procedure for stochastic multicriteria decision problems

A new interactive technique for a discrete stochastic multiattribute decision making problem is proposed in this paper. It is assumed that performance probability distribution for each action on each attribute is known. Two concepts are combined in the procedure: stochastic dominance and interactive approach. The first one is employed for generating efficient actions and constructing rankings of actions with respect to attributes. The second concept is used when the communication between the DM and the model is conducted. It is assumed that decision maker’s restrictions are defined by specifying minimal or maximal values of scalar criteria measuring either expected outcome or variability of outcomes. As such restrictions are, in general, not consistent with stochastic dominance rules, we suggest verifying this consistency and asking the decision maker to redefine inconsistent restrictions.     

Some problems in the analysis of stochastic dominance

The use of stochastic dominance has become common in finance and economics. As a theoretical device it is used to define a preference relation on a set of decision alternatives, thereby reducing the number of these alternatives which must be considered further by the decision-maker. However, in practice, data must be collected to estimate probability distributions. The paper discusses the errors which may result and the computation of their probabilities. The connection with statistical hypothesis testing is discussed.     

On the relative efficiency of nth order and DARA stochastic dominance rules

It is known that third order stochastic dominance implies DARA dominance while no implications exist between higher orders and DARA dominance. A recent contribution points out that, with regard to the problem of determining lower and upper bounds for the price of a financial option, the DARA rule turns out to improve the stochastic dominance criteria of any order. In this paper the relative efficiency of the ordinary stochastic dominance and DARA criteria for alternatives with discrete distributions are compared, in order to see if the better performance of DARA criterion is also suitable for other practical applications. Moreover, the operational use of the stochastic dominance techniques for financial choices is deepened.     

An extended maximin approach for decision analysis with uncontrollable factors

In this paper an extension to the maximin approach to decision analysis in the presence of uncontrollable factors is proposed. This extension is based on the assumption that probabilities of consequences are known. Using the language of stochastic dominance, one decision alternative is preferred to another if the cumulative distribution function of the first alternative dominates that of the second in some area of low value consequences. This approach is an extension of a standard lexicographic maximin procedure to a case in which decision alternatives are characterised by arbitrary, including continuous, sets of consequences. Applications of the suggested approach to an ‘attack–defence’ type game and to the problems of location of public facilities are discussed.     

Stochastic dominance with linear partial information

This paper is concerned with expanding the range of application of stochastic dominance as a basis for choosing between alternative decision strategies. Unlike most work in this area, it does so on the assumption that the decision maker is not willing to specify a unique subjective probability distribution for future states of nature, but is only able to articulate a fuzzy, inexact set of beliefs, which may be summarized by appropriate linear constraints on probabilities of events. It is shown that the concept of stochastic dominance readily transfers to this decision environment and that the relevant calculations are quite straightforward. Additionally, the requirements for stochastic dominance and for statistical dominance are compared; the existence of the former is shown always to imply the latter.     

基于随机参照点的均值-风险分析

本文依据参照依赖偏好模型提出了基于随机参照点的风险度量方法,进而构建了均值-风险模型,并讨论了该决策方法与随机占优之间的一致性。研究发现,该决策方法不仅与一级随机占优是一致的而且与二级随机占优也是一致的。由于二级随机占优与期望效用理论的一致性,因而所构建的均值-风险模型与期望效用理论也是一致的。     

Dominance-based Rough Set Approach to decision under uncertainty and time preference

We consider a problem of decision under uncertainty with outcomes distributed over time. We propose a rough set model based on a combination of time dominance and stochastic dominance. For the sake of simplicity we consider the case of traditional additive probability distribution over the set of states of the world, however, we show that the model is rich enough to handle non-additive probability distributions, and even qualitative ordinal distributions. The rough set approach gives a representation of decision maker’s time-dependent preferences under uncertainty in terms of “if…, then…” decision rules induced from rough approximations of sets of exemplary decisions.     

Operational asymptotic stochastic dominance

Levy (2016) proposes asymptotic first-degree stochastic dominance as a distribution ranking criterion for all non-satiable decision makers with infinite investment horizons. Given Levy’s setting, this paper defines and offers the equivalent distributional conditions for asymptotic second-degree stochastic dominance, as well as operational asymptotic first- and second-degree stochastic dominance. Interestingly, the operational asymptotic stochastic dominance provides a full rank over assets with lognormal returns and different means. Empirical applications show that our conditions can be readily implemented in practice.     

一种属性信息不完全的TODIM决策方法

针对属性信息不完全的多属性决策问题,考虑到决策者具有参照依赖和损失规避行为,提出一种不完全信息的TODIM决策方法。首先,在考虑决策者参照依赖和损失规避行为的基础上,计算每个方案相对于其它方案关于每个属性的优势度;然后计算每个方案相对于其它所有方案的总体优势度;再以最大化所有方案的总体优势度作为目标函数,建立确定最优方案的优化模型。进一步,利用TODIM方法的思想,计算每个方案相对于其它所有方案的总体优势度,从而对方案进行排序。最后通过一个风险投资的算例验证该方法的可行性和有效性。     

Stochastic dominance in an ordinal world

Existing stochastic dominance rules apply to variables such as income, wealth and rates of return, all of which are measured on cardinal scales. This study develops and applies stochastic dominance rules for ordinal data. It is shown that the new rules are consistent with the traditional von Neumann-Morgenstern expected utility approach, and that they are applicable and relevant in a wide variety of managerial decision making situations, where existing stochastic dominance rules fail to apply. We apply ordinal SD rules to the transformation of random variables.     

随机偏爱群体决策的随机Borda数法

对于具随机偏爱信息的群体决策问题，本文引入供选方案的随机Borda数和供选方案集上的随机Borda数映射概念．在讨论了随机Borda数映射满足随机偏爱公理的基础上，给出一个对所有供选方案进行群体排序的方法．     

Implausible alternatives in eliciting multi-attribute value functions

Many methods to elicit preference models in multi-attribute decision making rely on evaluations of a set of sample alternatives by decision makers. Using orthogonal design methods to create this set of alternatives might require respondents to evaluate unrealistic alternatives. In this paper, we perform an empirical study to analyze whether the presence of such implausible alternatives has an effect on the quality of utility elicitation. Using a new approach to measure consistency, we find that implausible alternatives in fact, have a positive effect on consistency of intra-attribute preference information and consistency with dominance, but do not affect inter-attribute preference information.     

The non-integer higher-order Stochastic dominance

We establish a continuum of higher-order stochastic dominance rules. Depending on whether decision makers are risk averse or risk loving, two types of continuum higher-order stochastic dominance rules are established and their properties are illustrated with some examples.     

Establishing Dominance in Multiattribute Decision Making Using an Ordered Metric Method

The paper is concerned with establishing dominance in linear decision models, either multiattribute models or models of decision making under uncertainty. It argues that a simple procedure, based on an ordered metric concept, provides a straightforward way of increasing the chance of locating patterns of dominance among alternatives. One particular application which is explored is the appraisal of competing investments.     

Probability dominance in random outcomes

An intuitively appealing probability dominance relation for random outcomes is defined and investigated. Its properties, strengths, and weaknesses relative to stochastic dominance and meanvariance dominance are studied. It is shown that the proposed probability dominance complements existing solution concepts and strengthens one's confidence in decision making under uncertainty. Fundamental characteristics of nondominated random payoffs and methods for identifying them, for both general and specific classes, are reported. Application pitfalls and possible extensions are also discussed.     

Compromise programming with Tchebycheff norm for discrete stochastic orders

This paper presents a method of decision making with returns in the form of discrete random variables. The proposed method is based on two approaches: stochastic orders and compromise programming used in multi-objective programming. Stochastic orders are represented by stochastic dominance and inverse stochastic dominance. Compromise programming uses the augmented Tchebycheff norm. This norm, in special cases, takes form of the Kantorovich and Kolmogorov probability metrics. Moreover, in the paper we show applications of the presented methodology in the following problems: projects selections, decision tree and choosing a lottery.